Knowledge, Psychological Impacts, and Protective Behaviors During the First Wave of COVID-19 Pandemic Among Chinese Immigrants with School-Age Children in Canada
Bibliographic record
Abstract
Abstract Background : The impact of COVID-19 on the emotions and behaviors of overseas Chinese immigrants and their families living in Canada has been poorly studied. The purpose of this study was to describe the knowledge, protective behaviors, and psychological impact of COVID-19 on Chinese immigrants and determine whether having school-age children was associated with adverse psychological outcomes. Methods: Using an online survey of 757 Chinese immigrants in Canada from April 2020, data regarding the perceptions of COVID-19, psychological impact, protective behaviors, and sociodemographic characteristics were collected and analyzed. A total of 747 eligible respondents were finally included in the analysis. Most of the participants (65.8%) were female and 77.2% had a university degree or higher. Results: There were no significant differences in knowledge of COVID-19 in participants with or without children aged 16 years or under. Participants with children aged 16 years or under were more likely to perceive themselves as being at greater risk of contracting COVID-19 than those without (P=0.023). Participants with children aged 16 years and under were also more likely to feel depressed (P = 0.007) or stressed (P = 0.010). In addition, parents with children aged 16 years and under were more likely to adopt protective behaviors, for example, washing and sanitizing hands frequently or disinfecting work and living spaces. Conclusions: For the most part, Chinese immigrants with children aged 16 years and under were more prone to negative emotions, such as stress, anxiety, and fear. These findings may assist key stakeholders with the identification and implementation of policies and interventions to support the needs of parents with young children, during and after the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".